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SFSDAF: an enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion

Li, Xiadong; Foody, Giles M.; Boyd, Doreen S.; Ge, Yong; Zhang, Yihang; Du, Yun; Ling, Feng

SFSDAF: an enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion Thumbnail


Authors

Xiadong Li

GILES FOODY giles.foody@nottingham.ac.uk
Professor of Geographical Information

DOREEN BOYD doreen.boyd@nottingham.ac.uk
Professor of Earth Observation

Yong Ge

Yihang Zhang

Yun Du

Feng Ling



Abstract

Spatio-temporal image fusion methods have become a popular means to produce remotely sensed data sets that have both fine spatial and temporal resolution. Accurate prediction of reflectance change is difficult, especially when the change is caused by both phenological change and land cover class changes. Although several spatio-temporal fusion methods such as the Flexible Spatiotemporal DAta Fusion (FSDAF) directly derive land cover phenological change information (such as endmember change) at different dates, the direct derivation of land cover class change information is challenging. In this paper, an enhanced FSDAF that incorporates sub-pixel class fraction change information (SFSDAF) is proposed. By directly deriving the sub-pixel land cover class fraction change information the proposed method allows accurate prediction even for heterogeneous regions that undergo a land cover class change. In particular, SFSDAF directly derives fine spatial resolution endmember change and class fraction change at the date of the observed image pair and the date of prediction, which can help identify image reflectance change resulting from different sources. SFSDAF predicts a fine resolution image at the time of acquisition of coarse resolution images using only one prior coarse and fine resolution image pair, and accommodates variations in reflectance due to both natural fluctuations in class spectral response (e.g. due to phenology) and land cover class change. The method is illustrated using degraded and real images and compared against three established spatio-temporal methods. The results show that the SFSDAF produced the least blurred images and the most accurate predictions of fine resolution reflectance values, especially for regions of heterogeneous landscape and regions that undergo some land cover class change. Consequently, the SFSDAF has considerable potential in monitoring Earth surface dynamics.

Journal Article Type Article
Acceptance Date Nov 11, 2019
Online Publication Date Nov 26, 2019
Publication Date 2020-02
Deposit Date Nov 22, 2019
Publicly Available Date Nov 27, 2020
Journal Remote Sensing of Environment
Print ISSN 0034-4257
Electronic ISSN 1879-0704
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 237
Article Number 111537
DOI https://doi.org/10.1016/j.rse.2019.111537
Keywords Spatio-temporal image fusion, Land cover class fraction, FSDAF
Public URL https://nottingham-repository.worktribe.com/output/3354550
Publisher URL https://www.sciencedirect.com/science/article/pii/S0034425719305565
Additional Information This article is maintained by: Elsevier; Article Title: SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion; Journal Title: Remote Sensing of Environment; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.rse.2019.111537; Content Type: article; Copyright: © 2019 Elsevier Inc. All rights reserved.

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